#import libraries
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

df = pd.read_csv('Datasets/Chicago_Crimes.csv')

df

df.columns

Index(['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type',
       'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat',
       'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate',
       'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude',
       'Location'],
      dtype='object')

top_types = df['Primary Type'].value_counts().head(10)
plt.figure(figsize=(10,6))
sns.barplot(x=top_types.values, y=top_types.index, palette='viridis')
plt.title('Top 10 Most Common Primary Crime Types')
plt.xlabel('Number of Crimes')
plt.ylabel('Primary Type')
plt.tight_layout()
plt.show()

df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
df['YearMonth'] = df['Date'].dt.to_period('M')

monthly_trend = df.groupby('YearMonth').size()

plt.figure(figsize=(14,6))
monthly_trend.plot(kind='line', marker='o', color='teal')
plt.title('Monthly Crime Trend in Chicago')
plt.xlabel('Year-Month')
plt.ylabel('Number of Crimes')
plt.grid(True, linestyle='--', alpha=0.5)
plt.tight_layout()
plt.show()

top_locations = df['Location Description'].value_counts().head(10)

plt.figure(figsize=(10,6))
sns.barplot(x=top_locations.values, y=top_locations.index, palette='magma')
plt.title('Top 10 Locations with Highest Number of Crimes')
plt.xlabel('Number of Crimes')
plt.ylabel('Location Description')
plt.tight_layout()
plt.show()

df['Arrest'] = df['Arrest'].astype(int)
df['Domestic'] = df['Domestic'].astype(int)
corr = df[['Arrest', 'Domestic']].corr().iloc[0,1]

plt.figure(figsize=(4,4))
sns.heatmap(df[['Arrest', 'Domestic']].corr(), annot=True, cmap='coolwarm')
plt.title(f'Correlation: Arrest vs Domestic ({corr:.2f})')
plt.show()

df['Weekday'] = df['Date'].dt.day_name()
weekday_counts = df['Weekday'].value_counts().reindex(
    ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
)

plt.figure(figsize=(8,5))
sns.barplot(x=weekday_counts.index, y=weekday_counts.values, palette='Set2')
plt.title('Average Number of Crimes per Weekday')
plt.ylabel('Number of Crimes')
plt.xlabel('Weekday')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

df['Date'] = pd.to_datetime(df['Date'])


daily_counts = df.groupby(df['Date'].dt.date).size()
plt.figure(figsize=(12,5))
daily_counts.plot(kind='line')
plt.title('Crimes per Day')
plt.xlabel('Date')
plt.ylabel('Number of Crimes')
plt.tight_layout()
plt.show()

df.columns

Index(['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type',
       'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat',
       'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate',
       'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude',
       'Location', 'YearMonth', 'Weekday'],
      dtype='object')

df

description = df['Description'].value_counts().head(10)

plt.figure(figsize=(8,5))
sns.barplot(x=description.values, y=description.index, palette='Blues_r')
plt.title('Top 5 Most Common Crime Descriptions')
plt.xlabel('Number of Crimes')
plt.ylabel('Description')
plt.tight_layout()
plt.show()

df['Year'] = df['Date'].dt.year
arrest_rate = df.groupby('Year')['Arrest'].mean()

plt.figure(figsize=(10,5))
arrest_rate.plot(marker='o', color='purple')
plt.title('Arrest Rate Trend Over Years')
plt.xlabel('Year')
plt.ylabel('Arrest Rate')
plt.tight_layout()
plt.show()

df

domestic_types = df[df['Domestic']==1]['Primary Type'].value_counts().head(5)
nondomestic_types = df[df['Domestic']==0]['Primary Type'].value_counts().head(5)

fig, ax = plt.subplots(1,2, figsize=(14,5))
sns.barplot(x=domestic_types.values, y=domestic_types.index, ax=ax[0], palette='Reds')
ax[0].set_title('Top 5 Domestic Crime Types')
ax[0].set_xlabel('Count')
sns.barplot(x=nondomestic_types.values, y=nondomestic_types.index, ax=ax[1], palette='Greens')
ax[1].set_title('Top 5 Non-Domestic Crime Types')
ax[1].set_xlabel('Count')
plt.tight_layout()
plt.show()

crime_by_type_date = df.groupby(['Date', 'Primary Type']).size().unstack(fill_value=0)

top_domestic = ['BATTERY', 'ASSAULT', 'OTHER OFFENSE', 'CRIMINAL DAMAGE', 'THEFT']
top_nondomestic = ['THEFT', 'BATTERY', 'CRIMINAL DAMAGE', 'MOTOR VEHICLE THEFT', 'ASSAULT']
selected_types = list(set(top_domestic + top_nondomestic))

filtered_crime = crime_by_type_date[selected_types]
correlation_matrix = filtered_crime.corr()
print(correlation_matrix)

Primary Type          BATTERY     THEFT  OTHER OFFENSE   ASSAULT  \
Primary Type                                                       
BATTERY              1.000000  0.230392       0.074937  0.069857   
THEFT                0.230392  1.000000       0.219587  0.188498   
OTHER OFFENSE        0.074937  0.219587       1.000000  0.068052   
ASSAULT              0.069857  0.188498       0.068052  1.000000   
MOTOR VEHICLE THEFT  0.256005  0.426658       0.174815  0.170638   
CRIMINAL DAMAGE      0.193837  0.343249       0.149269  0.126951   

Primary Type         MOTOR VEHICLE THEFT  CRIMINAL DAMAGE  
Primary Type                                               
BATTERY                         0.256005         0.193837  
THEFT                           0.426658         0.343249  
OTHER OFFENSE                   0.174815         0.149269  
ASSAULT                         0.170638         0.126951  
MOTOR VEHICLE THEFT             1.000000         0.352623  
CRIMINAL DAMAGE                 0.352623         1.000000

df

apartment_df = df[df['Location Description'] == 'APARTMENT']
apartment_df['Date'] = pd.to_datetime(apartment_df['Date'])
monthly_apartment = apartment_df.groupby(apartment_df['Date'].dt.to_period('M')).size()

plt.figure(figsize=(10,5))
monthly_apartment.plot(marker='o')
plt.title('Monthly Crimes in Apartments')
plt.xlabel('Month')
plt.ylabel('Number of Crimes')
plt.tight_layout()
plt.show()

df

gas_df = df[df['Location Description'] == 'GAS STATION']
gas_df['Arrest'] = gas_df['Arrest'].astype(int)
arrest_rate_gas = gas_df['Arrest'].mean()

plt.pie([arrest_rate_gas, 1-arrest_rate_gas], labels=['Arrested','Not Arrested'], autopct='%1.1f%%', colors=['#66b3ff','#ff9999'])
plt.title('Arrest Rate at Gas Stations')
plt.show()

df.columns

Index(['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type',
       'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat',
       'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate',
       'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude',
       'Location', 'YearMonth', 'Weekday'],
      dtype='object')

sidewalk_df = df[df['Location Description'] == 'SIDEWALK']
sidewalk_df['Domestic'] = sidewalk_df['Domestic'].astype(int)
domestic_counts = sidewalk_df['Domestic'].value_counts()

plt.bar(['Non-Domestic','Domestic'], domestic_counts.sort_index(), color=['#a1dab4','#41b6c4'])
plt.title('Domestic vs Non-Domestic Crimes on Sidewalks')
plt.ylabel('Number of Crimes')
plt.show()

df['Location Description'].unique()

array(['APARTMENT', 'COMMERCIAL / BUSINESS OFFICE', 'STREET', 'RESIDENCE',
       'RESIDENCE - PORCH / HALLWAY', 'RESTAURANT',
       'HOSPITAL BUILDING / GROUNDS', 'ATHLETIC CLUB',
       'PARKING LOT / GARAGE (NON RESIDENTIAL)', 'VEHICLE NON-COMMERCIAL',
       'SIDEWALK', 'OTHER (SPECIFY)', 'SCHOOL - PUBLIC BUILDING',
       'DRIVEWAY - RESIDENTIAL', 'BAR OR TAVERN', 'ALLEY', 'DRUG STORE',
       'SMALL RETAIL STORE', 'RESIDENCE - GARAGE', 'PARK PROPERTY',
       'CONVENIENCE STORE', 'HOTEL / MOTEL', 'SCHOOL - PUBLIC GROUNDS',
       'BOAT / WATERCRAFT', 'CHA PARKING LOT / GROUNDS',
       'POLICE FACILITY / VEHICLE PARKING LOT',
       'AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA',
       'AIRPORT PARKING LOT', 'AIRPORT EXTERIOR - NON-SECURE AREA',
       'AIRPORT TERMINAL UPPER LEVEL - SECURE AREA', 'BANK',
       'CTA PARKING LOT / GARAGE / OTHER PROPERTY', 'DEPARTMENT STORE',
       'VACANT LOT / LAND', 'CHURCH / SYNAGOGUE / PLACE OF WORSHIP',
       'NURSING / RETIREMENT HOME', 'GAS STATION',
       'RESIDENCE - YARD (FRONT / BACK)', 'GROCERY FOOD STORE',
       'CTA TRAIN', 'AIRPORT TERMINAL MEZZANINE - NON-SECURE AREA',
       'AIRPORT EXTERIOR - SECURE AREA', 'CEMETARY', 'CTA BUS',
       'CTA STATION', 'CTA PLATFORM', 'CHA APARTMENT',
       'AIRPORT TERMINAL LOWER LEVEL - SECURE AREA',
       'TAVERN / LIQUOR STORE', 'CONSTRUCTION SITE', 'WAREHOUSE',
       'CAR WASH', 'SCHOOL - PRIVATE GROUNDS', nan, 'DAY CARE CENTER',
       'AIRPORT TERMINAL LOWER LEVEL - NON-SECURE AREA',
       'FEDERAL BUILDING', 'GOVERNMENT BUILDING / PROPERTY',
       'VEHICLE - COMMERCIAL', 'COLLEGE / UNIVERSITY - GROUNDS',
       'AUTO / BOAT / RV DEALERSHIP', 'CTA BUS STOP', 'LIBRARY',
       'BARBERSHOP', 'TAXICAB', 'CHA HALLWAY / STAIRWELL / ELEVATOR',
       'ABANDONED BUILDING', 'MOVIE HOUSE / THEATER', 'BOWLING ALLEY',
       'APPLIANCE STORE', 'OTHER COMMERCIAL TRANSPORTATION',
       'SCHOOL - PRIVATE BUILDING',
       'AIRPORT BUILDING NON-TERMINAL - SECURE AREA',
       'VEHICLE - OTHER RIDE SHARE SERVICE (LYFT, UBER, ETC.)',
       'MEDICAL / DENTAL OFFICE', 'COIN OPERATED MACHINE',
       'CURRENCY EXCHANGE', 'JAIL / LOCK-UP FACILITY',
       'ATM (AUTOMATIC TELLER MACHINE)', 'AIRCRAFT',
       'OTHER RAILROAD PROPERTY / TRAIN DEPOT', 'FIRE STATION',
       'VACANT LOT', 'AIRPORT BUILDING NON-TERMINAL - NON-SECURE AREA',
       'HOUSE', 'SPORTS ARENA / STADIUM',
       'LAKEFRONT / WATERFRONT / RIVERBANK', 'DRIVEWAY', 'CLEANING STORE',
       'ANIMAL HOSPITAL', 'BRIDGE', 'HIGHWAY / EXPRESSWAY',
       'FACTORY / MANUFACTURING BUILDING', 'VEHICLE - DELIVERY TRUCK',
       'PAWN SHOP', 'PARKING LOT', 'PORCH', 'AUTO',
       'VEHICLE - COMMERCIAL: TROLLEY BUS',
       'COLLEGE / UNIVERSITY - RESIDENCE HALL',
       'AIRPORT TRANSPORTATION SYSTEM (ATS)',
       'AIRPORT VENDING ESTABLISHMENT', 'YARD', 'CREDIT UNION',
       'POOL ROOM', 'FOREST PRESERVE',
       'VEHICLE - COMMERCIAL: ENTERTAINMENT / PARTY BUS', 'CTA PROPERTY',
       'CHA GROUNDS', 'HOSPITAL', 'NEWSSTAND', 'FARM', 'TAVERN',
       'BASEMENT', 'CASINO/GAMBLING ESTABLISHMENT', 'KENNEL',
       'CTA TRACKS - RIGHT OF WAY', 'CHA HALLWAY', 'GANGWAY',
       'BARBER SHOP/BEAUTY SALON', 'RAILROAD PROPERTY', 'OFFICE',
       'HALLWAY', 'STAIRWELL', 'SAVINGS AND LOAN', 'RETAIL STORE',
       'HOTEL', 'CHA STAIRWELL', 'CTA "L" PLATFORM'], dtype=object)

parking_df = df[df['Location Description'] == 'AIRPORT PARKING LOT']
top_parking_types = parking_df['Primary Type'].value_counts().head(10)

plt.figure(figsize=(20,10))
plt.bar(top_parking_types.index, top_parking_types.values, color='orange')
plt.title('Crime Types in Airport Parking Lot')
plt.xlabel('Number of Crimes')
plt.tight_layout()
plt.show()

residence_df = df[df['Location Description'] == 'RESIDENCE']
residence_df['Date'] = pd.to_datetime(residence_df['Date'])
residence_df['Hour'] = residence_df['Date'].dt.hour
hourly_counts = residence_df['Hour'].value_counts().sort_index()

plt.plot(hourly_counts.index, hourly_counts.values, marker='o')
plt.title('Crimes by Hour in Residences')
plt.xlabel('Hour of Day')
plt.ylabel('Number of Crimes')
plt.show()

df

street_df = df[df['Location Description'] == 'STREET']
street_df['Date'] = pd.to_datetime(street_df['Date'])
daily_street = street_df.groupby(street_df['Date'].dt.date).size()
max_street_day = daily_street.idxmax()
max_street_value = daily_street.max()

plt.figure(figsize=(12,5))
daily_street.plot()
plt.axvline(max_street_day, color='red', linestyle='--', label=f'Max: {max_street_day} ({max_street_value})')
plt.title('Daily Crimes on Streets')
plt.xlabel('Date')
plt.ylabel('Number of Crimes')
plt.legend()
plt.tight_layout()
plt.show()

df['Location Description'].unique()

array(['APARTMENT', 'COMMERCIAL / BUSINESS OFFICE', 'STREET', 'RESIDENCE',
       'RESIDENCE - PORCH / HALLWAY', 'RESTAURANT',
       'HOSPITAL BUILDING / GROUNDS', 'ATHLETIC CLUB',
       'PARKING LOT / GARAGE (NON RESIDENTIAL)', 'VEHICLE NON-COMMERCIAL',
       'SIDEWALK', 'OTHER (SPECIFY)', 'SCHOOL - PUBLIC BUILDING',
       'DRIVEWAY - RESIDENTIAL', 'BAR OR TAVERN', 'ALLEY', 'DRUG STORE',
       'SMALL RETAIL STORE', 'RESIDENCE - GARAGE', 'PARK PROPERTY',
       'CONVENIENCE STORE', 'HOTEL / MOTEL', 'SCHOOL - PUBLIC GROUNDS',
       'BOAT / WATERCRAFT', 'CHA PARKING LOT / GROUNDS',
       'POLICE FACILITY / VEHICLE PARKING LOT',
       'AIRPORT TERMINAL UPPER LEVEL - NON-SECURE AREA',
       'AIRPORT PARKING LOT', 'AIRPORT EXTERIOR - NON-SECURE AREA',
       'AIRPORT TERMINAL UPPER LEVEL - SECURE AREA', 'BANK',
       'CTA PARKING LOT / GARAGE / OTHER PROPERTY', 'DEPARTMENT STORE',
       'VACANT LOT / LAND', 'CHURCH / SYNAGOGUE / PLACE OF WORSHIP',
       'NURSING / RETIREMENT HOME', 'GAS STATION',
       'RESIDENCE - YARD (FRONT / BACK)', 'GROCERY FOOD STORE',
       'CTA TRAIN', 'AIRPORT TERMINAL MEZZANINE - NON-SECURE AREA',
       'AIRPORT EXTERIOR - SECURE AREA', 'CEMETARY', 'CTA BUS',
       'CTA STATION', 'CTA PLATFORM', 'CHA APARTMENT',
       'AIRPORT TERMINAL LOWER LEVEL - SECURE AREA',
       'TAVERN / LIQUOR STORE', 'CONSTRUCTION SITE', 'WAREHOUSE',
       'CAR WASH', 'SCHOOL - PRIVATE GROUNDS', nan, 'DAY CARE CENTER',
       'AIRPORT TERMINAL LOWER LEVEL - NON-SECURE AREA',
       'FEDERAL BUILDING', 'GOVERNMENT BUILDING / PROPERTY',
       'VEHICLE - COMMERCIAL', 'COLLEGE / UNIVERSITY - GROUNDS',
       'AUTO / BOAT / RV DEALERSHIP', 'CTA BUS STOP', 'LIBRARY',
       'BARBERSHOP', 'TAXICAB', 'CHA HALLWAY / STAIRWELL / ELEVATOR',
       'ABANDONED BUILDING', 'MOVIE HOUSE / THEATER', 'BOWLING ALLEY',
       'APPLIANCE STORE', 'OTHER COMMERCIAL TRANSPORTATION',
       'SCHOOL - PRIVATE BUILDING',
       'AIRPORT BUILDING NON-TERMINAL - SECURE AREA',
       'VEHICLE - OTHER RIDE SHARE SERVICE (LYFT, UBER, ETC.)',
       'MEDICAL / DENTAL OFFICE', 'COIN OPERATED MACHINE',
       'CURRENCY EXCHANGE', 'JAIL / LOCK-UP FACILITY',
       'ATM (AUTOMATIC TELLER MACHINE)', 'AIRCRAFT',
       'OTHER RAILROAD PROPERTY / TRAIN DEPOT', 'FIRE STATION',
       'VACANT LOT', 'AIRPORT BUILDING NON-TERMINAL - NON-SECURE AREA',
       'HOUSE', 'SPORTS ARENA / STADIUM',
       'LAKEFRONT / WATERFRONT / RIVERBANK', 'DRIVEWAY', 'CLEANING STORE',
       'ANIMAL HOSPITAL', 'BRIDGE', 'HIGHWAY / EXPRESSWAY',
       'FACTORY / MANUFACTURING BUILDING', 'VEHICLE - DELIVERY TRUCK',
       'PAWN SHOP', 'PARKING LOT', 'PORCH', 'AUTO',
       'VEHICLE - COMMERCIAL: TROLLEY BUS',
       'COLLEGE / UNIVERSITY - RESIDENCE HALL',
       'AIRPORT TRANSPORTATION SYSTEM (ATS)',
       'AIRPORT VENDING ESTABLISHMENT', 'YARD', 'CREDIT UNION',
       'POOL ROOM', 'FOREST PRESERVE',
       'VEHICLE - COMMERCIAL: ENTERTAINMENT / PARTY BUS', 'CTA PROPERTY',
       'CHA GROUNDS', 'HOSPITAL', 'NEWSSTAND', 'FARM', 'TAVERN',
       'BASEMENT', 'CASINO/GAMBLING ESTABLISHMENT', 'KENNEL',
       'CTA TRACKS - RIGHT OF WAY', 'CHA HALLWAY', 'GANGWAY',
       'BARBER SHOP/BEAUTY SALON', 'RAILROAD PROPERTY', 'OFFICE',
       'HALLWAY', 'STAIRWELL', 'SAVINGS AND LOAN', 'RETAIL STORE',
       'HOTEL', 'CHA STAIRWELL', 'CTA "L" PLATFORM'], dtype=object)

school_df = df[df['Location Description'] == 'SCHOOL - PUBLIC GROUNDS']
school_df['Arrest'] = school_df['Arrest'].astype(int)
arrest_prop = school_df['Arrest'].value_counts(normalize=True)

plt.bar(['Not Arrested','Arrested'], arrest_prop.sort_index(), color=['#fdae61','#2b83ba'])
plt.title('Proportion of Arrests in Public School Buildings')
plt.ylabel('Proportion')
plt.show()

df

resto_df = df[df['Location Description'] == 'RESTAURANT']
resto_df['Date'] = pd.to_datetime(resto_df['Date'])
resto_monthly = resto_df.groupby(resto_df['Date'].dt.to_period('M')).size()
mean_resto = resto_monthly.mean()

plt.bar(resto_monthly.index.astype(str), resto_monthly.values, color='green')
plt.title('Monthly Crimes in Restaurants')
plt.xlabel('Month')
plt.ylabel('Number of Crimes')
plt.xticks(rotation=45)
plt.legend()
plt.tight_layout()
plt.show()

bar_df = df[df['Location Description'] == 'BAR OR TAVERN']
desc_counts = bar_df['Description'].value_counts().head(7)

plt.pie(desc_counts.values, labels=desc_counts.index, autopct='%1.1f%%', startangle=140)
plt.title('Top Crime Descriptions in Bars/Taverns')
plt.tight_layout()
plt.show()

dept_df = df[(df['Location Description'] == 'DEPARTMENT STORE') & (df['Primary Type'] == 'THEFT')]
dept_df['Date'] = pd.to_datetime(dept_df['Date'])
dept_df['Year'] = dept_df['Date'].dt.year
yearly_theft = dept_df.groupby('Year').size()

plt.bar(yearly_theft.index, yearly_theft.values, color='#b2182b')
plt.title('Yearly THEFT Crimes in Department Stores')
plt.xlabel('Year')
plt.ylabel('Number of THEFT Crimes')
plt.tight_layout()
plt.show()

df

theft_df = df[df['Primary Type'] == 'THEFT']
theft_df['Date'] = pd.to_datetime(theft_df['Date'])
monthly_theft = theft_df.groupby(theft_df['Date'].dt.to_period('M')).size()

plt.figure(figsize=(10,5))
monthly_theft.plot(marker='o', color='red')
plt.title('Monthly THEFT Crimes')
plt.xlabel('Month')
plt.ylabel('Number of THEFT Crimes')
plt.tight_layout()
plt.show()

battery_df = df[df['Primary Type'] == 'BATTERY']
top_battery_desc = battery_df['Description'].value_counts().head(5)

plt.barh(top_battery_desc.index, top_battery_desc.values, color='purple')
plt.title('BATTERY Crime')
plt.xlabel('Number of Crimes')
plt.tight_layout()
plt.show()

narcotics_df = df[df['Primary Type'] == 'NARCOTICS']
narcotics_df['Arrest'] = narcotics_df['Arrest'].astype(int)
arrest_prop = narcotics_df['Arrest'].value_counts(normalize=True)

plt.pie(arrest_prop, labels=['Not Arrested','Arrested'], autopct='%1.1f%%', colors=['#fdae61','#2b83ba'])
plt.title('Arrest Proportion for NARCOTICS Crimes')
plt.show()

damage_df = df[df['Primary Type'] == 'CRIMINAL DAMAGE']
top_damage_desc = damage_df['Description'].value_counts().head(7)

plt.bar(top_damage_desc.index, top_damage_desc.values, color='orange')
plt.title('CRIMINAL DAMAGE')
plt.ylabel('Number of Crimes')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

damage_df = df[df['Primary Type'] == 'CRIMINAL DAMAGE']
damage_df['Date'] = pd.to_datetime(damage_df['Date'])
damage_df['Day'] = damage_df['Date'].dt.date
daily_damage = damage_df.groupby(['Day', 'Description']).size().unstack(fill_value=0)
selected = daily_damage[['TO PROPERTY', 'TO VEHICLE']]
correlation = selected.corr()

sns.heatmap(correlation, annot=True, cmap='coolwarm')
plt.title('Correlation between Property and Vehicle Damage')
plt.show()

assault_df = df[df['Primary Type'] == 'ASSAULT']
assault_df['Date'] = pd.to_datetime(assault_df['Date'])
monthly_assault = assault_df.groupby(assault_df['Date'].dt.to_period('M')).size()

plt.plot(monthly_assault.index.astype(str), monthly_assault.values, marker='o', color='green')
plt.title('Monthly ASSAULT Crimes')
plt.xlabel('Month')
plt.ylabel('Number of ASSAULT Crimes')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

robbery_df = df[df['Primary Type'] == 'ROBBERY']
robbery_df['Date'] = pd.to_datetime(robbery_df['Date'])
daily_robbery = robbery_df.groupby(robbery_df['Date'].dt.date).size()
max_robbery_day = daily_robbery.idxmax()
max_robbery_value = daily_robbery.max()

plt.figure(figsize=(12,5))
daily_robbery.plot()
plt.title('Daily ROBBERY Crimes')
plt.xlabel('Date')
plt.ylabel('Number of ROBBERY Crimes')
plt.legend()
plt.tight_layout()
plt.show()

robbery_df = df[df['Primary Type'] == 'ROBBERY']
robbery_df

mvt_df = df[df['Primary Type'] == 'MOTOR VEHICLE THEFT']
top_mvt_desc = mvt_df['Description'].value_counts().head(6)

plt.barh(top_mvt_desc.index, top_mvt_desc.values, color='teal')
plt.title('Top MOTOR VEHICLE THEFT Descriptions')
plt.xlabel('Number of Crimes')
plt.tight_layout()
plt.show()

theft_df = df[df['Primary Type'] == 'THEFT']
top_theft_desc = theft_df['Description'].value_counts().head(3).index
arrest_rates = []
for desc in top_theft_desc:
    sub_df = theft_df[theft_df['Description'] == desc]
    sub_df['Arrest'] = sub_df['Arrest'].astype(int)
    arrest_rates.append(sub_df['Arrest'].mean())

plt.bar(top_theft_desc, arrest_rates, color='navy')
plt.title('Arrest Rate for Top 3 THEFT Descriptions')
plt.ylabel('Arrest Rate')
plt.ylim(0,1)
plt.tight_layout()
plt.show()

df

attery_df = df[df['Primary Type'] == 'BATTERY']
battery_df['Date'] = pd.to_datetime(battery_df['Date'])
battery_df['Weekday'] = battery_df['Date'].dt.day_name()
weekday_counts = battery_df['Weekday'].value_counts().reindex(
    ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
)

plt.bar(weekday_counts.index, weekday_counts.values, color='crimson')
plt.title('BATTERY Crimes per Weekday')
plt.ylabel('Number of Crimes')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

trespass_df = df[df['Primary Type'] == 'CRIMINAL TRESPASS']
trespass_df['Arrest'] = trespass_df['Arrest'].astype(int)
trespass_df['Domestic'] = trespass_df['Domestic'].astype(int)
corr = trespass_df[['Arrest', 'Domestic']].corr().iloc[0,1]

plt.figure(figsize=(4,4))
sns.heatmap(trespass_df[['Arrest', 'Domestic']].corr(), annot=True, cmap='coolwarm')
plt.title(f'Correlation: Arrest vs Domestic (TRESPASS) ({corr:.2f})')
plt.show()

df

import folium
from folium.plugins import HeatMap

df_map = df.dropna(subset=['Latitude', 'Longitude'])
m = folium.Map(location=[41.8781, -87.6298], zoom_start=11)
HeatMap(data=df_map[['Latitude', 'Longitude']].values, radius=8, blur=12, max_zoom=1).add_to(m)

legend_html = '''
 <div style="position: fixed; 
     bottom: 50px; left: 50px; width: 180px; height: 90px; 
     background-color: white; z-index:9999; font-size:14px;
     border:2px solid grey; border-radius:8px; padding: 10px;">
     <b>Crime Density Legend</b><br>
     <i style="background: #ffffb2; width: 20px; height: 10px; display: inline-block;"></i> High<br>
     <i style="background: #fd8d3c; width: 20px; height: 10px; display: inline-block;"></i> Medium<br>
     <i style="background: #bd0026; width: 20px; height: 10px; display: inline-block;"></i> Low
 </div>
'''
m.get_root().html.add_child(folium.Element(legend_html))
m
#I improve this code using ai to make the viewer determine what is the color for

df

top_locs = df['Location Description'].value_counts().head(5).index
arrest_rates = df[df['Location Description'].isin(top_locs)].groupby('Location Description')['Arrest'].mean().reindex(top_locs)

plt.figure(figsize=(10,5))
plt.bar(arrest_rates.index, arrest_rates.values, color='royalblue')
plt.title('Arrest Rate for Locations')
plt.ylabel('Arrest Rate')
plt.ylim(0,1)
plt.tight_layout()
plt.show()

df

primary_type = 'WEAPONS VIOLATION'  # Change to any type you want
subset = df[df['Primary Type'] == primary_type]
arrest_rates = subset.groupby('Description')['Arrest'].mean().sort_values(ascending=False)

plt.figure(figsize=(10,15))
arrest_rates.plot(kind='bar', color='firebrick')
plt.title(f'Arrest Rate by Description for {primary_type}')
plt.ylabel('Arrest Rate')
plt.ylim(0,1)
plt.tight_layout()
plt.show()

df

battery_df = df[df['Primary Type'] == 'BATTERY']
battery_df['Arrest'] = battery_df['Arrest'].astype(int)
battery_df['Domestic'] = battery_df['Domestic'].astype(int)
corr = battery_df[['Arrest', 'Domestic']].corr().iloc[0,1]

plt.figure(figsize=(4,4))
sns.heatmap(battery_df[['Arrest', 'Domestic']].corr(), annot=True, cmap='coolwarm')
plt.title(f'Correlation: Arrest vs Domestic (BATTERY) ({corr:.2f})')
plt.show()

	ID	Case Number	Date	Block	IUCR	Primary Type	Description	Location Description	Arrest	Domestic	...	Ward	Community Area	FBI Code	X Coordinate	Y Coordinate	Year	Updated On	Latitude	Longitude	Location
0	13439321	JH237424	04/14/2024 12:00:00 AM	040XX S PRAIRIE AVE	0890	THEFT	FROM BUILDING	APARTMENT	False	False	...	3	38.0	06	1178707.0	1878256.0	2024	12/21/2024 03:40:46 PM	41.821236	-87.619921	(41.821236024, -87.619920712)
1	13437420	JH234779	04/14/2024 12:00:00 AM	023XX W CERMAK RD	2825	OTHER OFFENSE	HARASSMENT BY TELEPHONE	COMMERCIAL / BUSINESS OFFICE	False	False	...	25	31.0	26	1161210.0	1889347.0	2024	12/21/2024 03:40:46 PM	41.852052	-87.683801	(41.852051675, -87.683800849)
2	13428676	JH224478	04/14/2024 12:00:00 AM	043XX W LE MOYNE ST	0917	MOTOR VEHICLE THEFT	CYCLE, SCOOTER, BIKE WITH VIN	STREET	False	False	...	36	23.0	07	1146960.0	1909501.0	2024	12/21/2024 03:40:46 PM	41.907640	-87.735587	(41.907640473, -87.735587478)
3	13429357	JH225293	04/14/2024 12:00:00 AM	039XX W ADAMS ST	143A	WEAPONS VIOLATION	UNLAWFUL POSSESSION - HANDGUN	STREET	True	False	...	28	26.0	15	1150158.0	1898721.0	2024	12/21/2024 03:40:46 PM	41.877997	-87.724121	(41.877997275, -87.724120826)
4	13430098	JH226395	04/14/2024 12:00:00 AM	011XX W 112TH PL	0890	THEFT	FROM BUILDING	RESIDENCE	False	False	...	21	75.0	06	1170856.0	1830157.0	2024	12/21/2024 03:40:46 PM	41.689421	-87.650123	(41.6894214, -87.650123247)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
249118	13805239	JJ217509	04/12/2025 12:00:00 AM	029XX W LOGAN BLVD	2826	OTHER OFFENSE	HARASSMENT BY ELECTRONIC MEANS	APARTMENT	False	False	...	1	22.0	26	1156478.0	1917149.0	2025	04/19/2025 03:41:24 PM	41.928440	-87.700416	(41.928439867, -87.700415972)
249119	13804023	JJ215813	04/12/2025 12:00:00 AM	094XX S HARVARD AVE	0430	BATTERY	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	False	False	...	9	49.0	04B	1175694.0	1842631.0	2025	04/19/2025 03:41:24 PM	41.723545	-87.632040	(41.723545182, -87.632039508)
249120	13803926	JJ215943	04/12/2025 12:00:00 AM	084XX S VINCENNES AVE	0486	BATTERY	DOMESTIC BATTERY SIMPLE	APARTMENT	False	True	...	21	71.0	08B	1173850.0	1848976.0	2025	04/19/2025 03:41:24 PM	41.740998	-87.638606	(41.74099774, -87.638606337)
249121	13803475	JJ215338	04/12/2025 12:00:00 AM	050XX S ABERDEEN ST	0530	ASSAULT	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	True	False	...	20	61.0	04A	1169838.0	1871348.0	2025	04/19/2025 03:41:24 PM	41.802477	-87.652657	(41.802477219, -87.652657244)
249122	13804512	JJ216668	04/12/2025 12:00:00 AM	012XX W CARROLL AVE	0710	THEFT	THEFT FROM MOTOR VEHICLE	STREET	False	False	...	27	28.0	06	1168216.0	1902390.0	2025	04/19/2025 03:41:24 PM	41.887694	-87.657710	(41.887694407, -87.657710204)

	ID	Case Number	Date	Block	IUCR	Primary Type	Description	Location Description	Arrest	Domestic	...	FBI Code	X Coordinate	Y Coordinate	Year	Updated On	Latitude	Longitude	Location	YearMonth	Weekday
0	13439321	JH237424	2024-04-14	040XX S PRAIRIE AVE	0890	THEFT	FROM BUILDING	APARTMENT	0	0	...	06	1178707.0	1878256.0	2024	12/21/2024 03:40:46 PM	41.821236	-87.619921	(41.821236024, -87.619920712)	2024-04	Sunday
1	13437420	JH234779	2024-04-14	023XX W CERMAK RD	2825	OTHER OFFENSE	HARASSMENT BY TELEPHONE	COMMERCIAL / BUSINESS OFFICE	0	0	...	26	1161210.0	1889347.0	2024	12/21/2024 03:40:46 PM	41.852052	-87.683801	(41.852051675, -87.683800849)	2024-04	Sunday
2	13428676	JH224478	2024-04-14	043XX W LE MOYNE ST	0917	MOTOR VEHICLE THEFT	CYCLE, SCOOTER, BIKE WITH VIN	STREET	0	0	...	07	1146960.0	1909501.0	2024	12/21/2024 03:40:46 PM	41.907640	-87.735587	(41.907640473, -87.735587478)	2024-04	Sunday
3	13429357	JH225293	2024-04-14	039XX W ADAMS ST	143A	WEAPONS VIOLATION	UNLAWFUL POSSESSION - HANDGUN	STREET	1	0	...	15	1150158.0	1898721.0	2024	12/21/2024 03:40:46 PM	41.877997	-87.724121	(41.877997275, -87.724120826)	2024-04	Sunday
4	13430098	JH226395	2024-04-14	011XX W 112TH PL	0890	THEFT	FROM BUILDING	RESIDENCE	0	0	...	06	1170856.0	1830157.0	2024	12/21/2024 03:40:46 PM	41.689421	-87.650123	(41.6894214, -87.650123247)	2024-04	Sunday
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
249118	13805239	JJ217509	2025-04-12	029XX W LOGAN BLVD	2826	OTHER OFFENSE	HARASSMENT BY ELECTRONIC MEANS	APARTMENT	0	0	...	26	1156478.0	1917149.0	2025	04/19/2025 03:41:24 PM	41.928440	-87.700416	(41.928439867, -87.700415972)	2025-04	Saturday
249119	13804023	JJ215813	2025-04-12	094XX S HARVARD AVE	0430	BATTERY	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	0	0	...	04B	1175694.0	1842631.0	2025	04/19/2025 03:41:24 PM	41.723545	-87.632040	(41.723545182, -87.632039508)	2025-04	Saturday
249120	13803926	JJ215943	2025-04-12	084XX S VINCENNES AVE	0486	BATTERY	DOMESTIC BATTERY SIMPLE	APARTMENT	0	1	...	08B	1173850.0	1848976.0	2025	04/19/2025 03:41:24 PM	41.740998	-87.638606	(41.74099774, -87.638606337)	2025-04	Saturday
249121	13803475	JJ215338	2025-04-12	050XX S ABERDEEN ST	0530	ASSAULT	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	1	0	...	04A	1169838.0	1871348.0	2025	04/19/2025 03:41:24 PM	41.802477	-87.652657	(41.802477219, -87.652657244)	2025-04	Saturday
249122	13804512	JJ216668	2025-04-12	012XX W CARROLL AVE	0710	THEFT	THEFT FROM MOTOR VEHICLE	STREET	0	0	...	06	1168216.0	1902390.0	2025	04/19/2025 03:41:24 PM	41.887694	-87.657710	(41.887694407, -87.657710204)	2025-04	Saturday

	ID	Case Number	Date	Block	IUCR	Primary Type	Description	Location Description	Arrest	Domestic	...	FBI Code	X Coordinate	Y Coordinate	Year	Updated On	Latitude	Longitude	Location	YearMonth	Weekday
0	13439321	JH237424	2024-04-14	040XX S PRAIRIE AVE	0890	THEFT	FROM BUILDING	APARTMENT	0	0	...	06	1178707.0	1878256.0	2024	12/21/2024 03:40:46 PM	41.821236	-87.619921	(41.821236024, -87.619920712)	2024-04	Sunday
1	13437420	JH234779	2024-04-14	023XX W CERMAK RD	2825	OTHER OFFENSE	HARASSMENT BY TELEPHONE	COMMERCIAL / BUSINESS OFFICE	0	0	...	26	1161210.0	1889347.0	2024	12/21/2024 03:40:46 PM	41.852052	-87.683801	(41.852051675, -87.683800849)	2024-04	Sunday
2	13428676	JH224478	2024-04-14	043XX W LE MOYNE ST	0917	MOTOR VEHICLE THEFT	CYCLE, SCOOTER, BIKE WITH VIN	STREET	0	0	...	07	1146960.0	1909501.0	2024	12/21/2024 03:40:46 PM	41.907640	-87.735587	(41.907640473, -87.735587478)	2024-04	Sunday
3	13429357	JH225293	2024-04-14	039XX W ADAMS ST	143A	WEAPONS VIOLATION	UNLAWFUL POSSESSION - HANDGUN	STREET	1	0	...	15	1150158.0	1898721.0	2024	12/21/2024 03:40:46 PM	41.877997	-87.724121	(41.877997275, -87.724120826)	2024-04	Sunday
4	13430098	JH226395	2024-04-14	011XX W 112TH PL	0890	THEFT	FROM BUILDING	RESIDENCE	0	0	...	06	1170856.0	1830157.0	2024	12/21/2024 03:40:46 PM	41.689421	-87.650123	(41.6894214, -87.650123247)	2024-04	Sunday
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
249118	13805239	JJ217509	2025-04-12	029XX W LOGAN BLVD	2826	OTHER OFFENSE	HARASSMENT BY ELECTRONIC MEANS	APARTMENT	0	0	...	26	1156478.0	1917149.0	2025	04/19/2025 03:41:24 PM	41.928440	-87.700416	(41.928439867, -87.700415972)	2025-04	Saturday
249119	13804023	JJ215813	2025-04-12	094XX S HARVARD AVE	0430	BATTERY	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	0	0	...	04B	1175694.0	1842631.0	2025	04/19/2025 03:41:24 PM	41.723545	-87.632040	(41.723545182, -87.632039508)	2025-04	Saturday
249120	13803926	JJ215943	2025-04-12	084XX S VINCENNES AVE	0486	BATTERY	DOMESTIC BATTERY SIMPLE	APARTMENT	0	1	...	08B	1173850.0	1848976.0	2025	04/19/2025 03:41:24 PM	41.740998	-87.638606	(41.74099774, -87.638606337)	2025-04	Saturday
249121	13803475	JJ215338	2025-04-12	050XX S ABERDEEN ST	0530	ASSAULT	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	1	0	...	04A	1169838.0	1871348.0	2025	04/19/2025 03:41:24 PM	41.802477	-87.652657	(41.802477219, -87.652657244)	2025-04	Saturday
249122	13804512	JJ216668	2025-04-12	012XX W CARROLL AVE	0710	THEFT	THEFT FROM MOTOR VEHICLE	STREET	0	0	...	06	1168216.0	1902390.0	2025	04/19/2025 03:41:24 PM	41.887694	-87.657710	(41.887694407, -87.657710204)	2025-04	Saturday

	ID	Case Number	Date	Block	IUCR	Primary Type	Description	Location Description	Arrest	Domestic	...	FBI Code	X Coordinate	Y Coordinate	Year	Updated On	Latitude	Longitude	Location	YearMonth	Weekday
0	13439321	JH237424	2024-04-14	040XX S PRAIRIE AVE	0890	THEFT	FROM BUILDING	APARTMENT	0	0	...	06	1178707.0	1878256.0	2024	12/21/2024 03:40:46 PM	41.821236	-87.619921	(41.821236024, -87.619920712)	2024-04	Sunday
1	13437420	JH234779	2024-04-14	023XX W CERMAK RD	2825	OTHER OFFENSE	HARASSMENT BY TELEPHONE	COMMERCIAL / BUSINESS OFFICE	0	0	...	26	1161210.0	1889347.0	2024	12/21/2024 03:40:46 PM	41.852052	-87.683801	(41.852051675, -87.683800849)	2024-04	Sunday
2	13428676	JH224478	2024-04-14	043XX W LE MOYNE ST	0917	MOTOR VEHICLE THEFT	CYCLE, SCOOTER, BIKE WITH VIN	STREET	0	0	...	07	1146960.0	1909501.0	2024	12/21/2024 03:40:46 PM	41.907640	-87.735587	(41.907640473, -87.735587478)	2024-04	Sunday
3	13429357	JH225293	2024-04-14	039XX W ADAMS ST	143A	WEAPONS VIOLATION	UNLAWFUL POSSESSION - HANDGUN	STREET	1	0	...	15	1150158.0	1898721.0	2024	12/21/2024 03:40:46 PM	41.877997	-87.724121	(41.877997275, -87.724120826)	2024-04	Sunday
4	13430098	JH226395	2024-04-14	011XX W 112TH PL	0890	THEFT	FROM BUILDING	RESIDENCE	0	0	...	06	1170856.0	1830157.0	2024	12/21/2024 03:40:46 PM	41.689421	-87.650123	(41.6894214, -87.650123247)	2024-04	Sunday
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
249118	13805239	JJ217509	2025-04-12	029XX W LOGAN BLVD	2826	OTHER OFFENSE	HARASSMENT BY ELECTRONIC MEANS	APARTMENT	0	0	...	26	1156478.0	1917149.0	2025	04/19/2025 03:41:24 PM	41.928440	-87.700416	(41.928439867, -87.700415972)	2025-04	Saturday
249119	13804023	JJ215813	2025-04-12	094XX S HARVARD AVE	0430	BATTERY	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	0	0	...	04B	1175694.0	1842631.0	2025	04/19/2025 03:41:24 PM	41.723545	-87.632040	(41.723545182, -87.632039508)	2025-04	Saturday
249120	13803926	JJ215943	2025-04-12	084XX S VINCENNES AVE	0486	BATTERY	DOMESTIC BATTERY SIMPLE	APARTMENT	0	1	...	08B	1173850.0	1848976.0	2025	04/19/2025 03:41:24 PM	41.740998	-87.638606	(41.74099774, -87.638606337)	2025-04	Saturday
249121	13803475	JJ215338	2025-04-12	050XX S ABERDEEN ST	0530	ASSAULT	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	1	0	...	04A	1169838.0	1871348.0	2025	04/19/2025 03:41:24 PM	41.802477	-87.652657	(41.802477219, -87.652657244)	2025-04	Saturday
249122	13804512	JJ216668	2025-04-12	012XX W CARROLL AVE	0710	THEFT	THEFT FROM MOTOR VEHICLE	STREET	0	0	...	06	1168216.0	1902390.0	2025	04/19/2025 03:41:24 PM	41.887694	-87.657710	(41.887694407, -87.657710204)	2025-04	Saturday

	ID	Case Number	Date	Block	IUCR	Primary Type	Description	Location Description	Arrest	Domestic	...	FBI Code	X Coordinate	Y Coordinate	Year	Updated On	Latitude	Longitude	Location	YearMonth	Weekday
0	13439321	JH237424	2024-04-14	040XX S PRAIRIE AVE	0890	THEFT	FROM BUILDING	APARTMENT	0	0	...	06	1178707.0	1878256.0	2024	12/21/2024 03:40:46 PM	41.821236	-87.619921	(41.821236024, -87.619920712)	2024-04	Sunday
1	13437420	JH234779	2024-04-14	023XX W CERMAK RD	2825	OTHER OFFENSE	HARASSMENT BY TELEPHONE	COMMERCIAL / BUSINESS OFFICE	0	0	...	26	1161210.0	1889347.0	2024	12/21/2024 03:40:46 PM	41.852052	-87.683801	(41.852051675, -87.683800849)	2024-04	Sunday
2	13428676	JH224478	2024-04-14	043XX W LE MOYNE ST	0917	MOTOR VEHICLE THEFT	CYCLE, SCOOTER, BIKE WITH VIN	STREET	0	0	...	07	1146960.0	1909501.0	2024	12/21/2024 03:40:46 PM	41.907640	-87.735587	(41.907640473, -87.735587478)	2024-04	Sunday
3	13429357	JH225293	2024-04-14	039XX W ADAMS ST	143A	WEAPONS VIOLATION	UNLAWFUL POSSESSION - HANDGUN	STREET	1	0	...	15	1150158.0	1898721.0	2024	12/21/2024 03:40:46 PM	41.877997	-87.724121	(41.877997275, -87.724120826)	2024-04	Sunday
4	13430098	JH226395	2024-04-14	011XX W 112TH PL	0890	THEFT	FROM BUILDING	RESIDENCE	0	0	...	06	1170856.0	1830157.0	2024	12/21/2024 03:40:46 PM	41.689421	-87.650123	(41.6894214, -87.650123247)	2024-04	Sunday
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
249118	13805239	JJ217509	2025-04-12	029XX W LOGAN BLVD	2826	OTHER OFFENSE	HARASSMENT BY ELECTRONIC MEANS	APARTMENT	0	0	...	26	1156478.0	1917149.0	2025	04/19/2025 03:41:24 PM	41.928440	-87.700416	(41.928439867, -87.700415972)	2025-04	Saturday
249119	13804023	JJ215813	2025-04-12	094XX S HARVARD AVE	0430	BATTERY	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	0	0	...	04B	1175694.0	1842631.0	2025	04/19/2025 03:41:24 PM	41.723545	-87.632040	(41.723545182, -87.632039508)	2025-04	Saturday
249120	13803926	JJ215943	2025-04-12	084XX S VINCENNES AVE	0486	BATTERY	DOMESTIC BATTERY SIMPLE	APARTMENT	0	1	...	08B	1173850.0	1848976.0	2025	04/19/2025 03:41:24 PM	41.740998	-87.638606	(41.74099774, -87.638606337)	2025-04	Saturday
249121	13803475	JJ215338	2025-04-12	050XX S ABERDEEN ST	0530	ASSAULT	AGGRAVATED - OTHER DANGEROUS WEAPON	STREET	1	0	...	04A	1169838.0	1871348.0	2025	04/19/2025 03:41:24 PM	41.802477	-87.652657	(41.802477219, -87.652657244)	2025-04	Saturday
249122	13804512	JJ216668	2025-04-12	012XX W CARROLL AVE	0710	THEFT	THEFT FROM MOTOR VEHICLE	STREET	0	0	...	06	1168216.0	1902390.0	2025	04/19/2025 03:41:24 PM	41.887694	-87.657710	(41.887694407, -87.657710204)	2025-04	Saturday

	ID	Case Number	Date	Block	IUCR	Primary Type	Description	Location Description	Arrest	Domestic	...	FBI Code	X Coordinate	Y Coordinate	Year	Updated On	Latitude	Longitude	Location	YearMonth	Weekday
37	13428473	JH224225	2024-04-14 00:05:00	008XX S MORGAN ST	0340	ROBBERY	ATTEMPT STRONG ARM - NO WEAPON	STREET	1	0	...	03	1170047.0	1896351.0	2024	12/21/2024 03:40:46 PM	41.871083	-87.651163	(41.871083204, -87.651162509)	2024-04	Sunday
57	13428954	JH224898	2024-04-14 00:30:00	047XX N KILPATRICK AVE	0320	ROBBERY	STRONG ARM - NO WEAPON	SIDEWALK	0	0	...	03	1144180.0	1930999.0	2024	12/21/2024 03:40:46 PM	41.966686	-87.745258	(41.966685706, -87.745258076)	2024-04	Sunday
95	13428564	JH224312	2024-04-14 01:25:00	004XX W 24TH ST	0320	ROBBERY	STRONG ARM - NO WEAPON	COMMERCIAL / BUSINESS OFFICE	0	1	...	03	1173417.0	1888352.0	2024	12/21/2024 03:40:46 PM	41.849059	-87.639028	(41.849059202, -87.639027598)	2024-04	Sunday
112	13428508	JH224290	2024-04-14 01:52:00	001XX N LAPORTE AVE	0320	ROBBERY	STRONG ARM - NO WEAPON	VEHICLE NON-COMMERCIAL	0	0	...	03	1143351.0	1900736.0	2024	12/21/2024 03:40:46 PM	41.883657	-87.749064	(41.883656549, -87.749064412)	2024-04	Sunday
148	13428659	JH224447	2024-04-14 02:33:00	0000X W DIVISION ST	0320	ROBBERY	STRONG ARM - NO WEAPON	STREET	0	0	...	03	1175906.0	1908369.0	2024	12/21/2024 03:40:46 PM	41.903931	-87.629290	(41.903931469, -87.629290187)	2024-04	Sunday
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
248924	13806459	JJ218927	2025-04-11 17:30:00	078XX S HERMITAGE AVE	031A	ROBBERY	ARMED - HANDGUN	ALLEY	0	0	...	03	1166043.0	1852683.0	2025	04/19/2025 03:41:24 PM	41.751340	-87.667105	(41.751339673, -87.667105463)	2025-04	Friday
249014	13803020	JJ214803	2025-04-11 20:30:00	047XX N LAWNDALE AVE	0312	ROBBERY	ARMED - KNIFE / CUTTING INSTRUMENT	ALLEY	0	0	...	03	1150866.0	1931287.0	2025	04/19/2025 03:41:24 PM	41.967347	-87.720667	(41.967347439, -87.720666884)	2025-04	Friday
249042	13803577	JJ214845	2025-04-11 21:40:00	001XX W LAKE ST	0320	ROBBERY	STRONG ARM - NO WEAPON	CTA TRAIN	0	0	...	03	1175334.0	1901735.0	2025	04/19/2025 03:41:24 PM	41.885740	-87.631591	(41.885740288, -87.631590568)	2025-04	Friday
249081	13803748	JJ215553	2025-04-11 23:00:00	019XX W MADISON ST	0320	ROBBERY	STRONG ARM - NO WEAPON	SIDEWALK	0	0	...	03	1163655.0	1900042.0	2025	04/19/2025 03:41:24 PM	41.881349	-87.674526	(41.881348623, -87.674525788)	2025-04	Friday
249099	13803128	JJ214903	2025-04-11 23:39:00	035XX N ORIOLE AVE	0334	ROBBERY	ATTEMPT ARMED - KNIFE / CUTTING INSTRUMENT	SIDEWALK	0	0	...	03	1124794.0	1922510.0	2025	04/19/2025 03:41:24 PM	41.943734	-87.816727	(41.943733887, -87.816727097)	2025-04	Friday

MIDTERM EXAM HANDS ON - CHICAGO CRIMES DATA ANALYTICS

Analyst:Ostaga,Christian Joseph

QUESTIONS

1.What are the top 10 most common primary crime types in Chicago?

Insight : Base on the bar plot we can see that the most common crime is theft

2. What are the monthly crime trends in Chicago?

Insight : Base on the plot we can see that july has the highest crime rate

3.Which locations have the highest number of reported crimes in Chicago?

Insight : Base On the bar plot we can see that the location that has a lot of crime happen is on the street

4.What is the correlation between arrest rates and domestic crimes?

Insigt : Base on the heatmap there is a correlation. But practically, it's so weak. A correlation of 0.0053 suggests almost no relationship between the two.

5. What is the average number of crimes per weekday?

Insight : Base on the barplot we can see that there are so many crimes per day the gap is so close its almost like every day the crime is the same

6. What is the maximum number of crimes reported in a single day?

Insight : Base on the plot we can see that the crime numbers were mostly steady, but at the end it suddenly dropped to zero this means that particular day no one did a crime i think there is a big event that time even the criminals did nothing.

7.What are the most common crime descriptions?

Insight : Base on the barplot we can see that the most common crime is SIMPLE, and it happened the most times. Other crimes include DOMESTIC BATTERY SIMPLE and $500 AND UNDER.

8. What is the trend of arrest rates over the years?

Insight : Base on the plot we can see that the arrest rate increased a lot we can see that on 2024 it is very low but every months pass the arrest rate in inscresing rapidly and not even dropping.

9.what is the distribution of crime types for domestic vs non-domestic cases?

10.what is the correlation between domestic and non domestic?

Insight for numbir 9 :Base on the 2 bar plot we can see that domestic crimes like battery and assault are very common crime in chicago, while non domestic crimes are mostly theft and damage. This shows that crime types differ depending on whether they happen at home or not.

Insight For number 10 : Base on the result we can see that there is a correlation between domestic and non domestic theres a few that has a very low correlation but still all of theresult show nothing is not correlated this mean there is a correllation between this two.

11.What is the number of crime on apartment monthly?

12.How many arrest rate on gas stations??

Insight : Base on the pie chart we can see that the arrest rate on the gas statio is just 22.8% which means there are only few people that get arrested on that particular location.

13.Number of Domestic vs Non-Domestic Crimes in Sidewalks.

Insight: Base on the bar graph we can see that theres a lot of non domestic crime on sidewalks like a lot because if we compare the two we can clearly see that domestic crimes is very low on sidewalks.

14.What are the crime types on Airport Parking Lot?

INsight: Base on the bar plot we can see that motor vehicle theft is the common crime on airport parking lot and robbery is too low it means that theres a lot of motor vehicle theft happening before and after flights.

15.Crimes by Hour in RESIDENCE.

Insight : Base on the plot most crimes in homes happen around 9 AM and 5 PM. That is when people are usually leaving or coming back so it might be easier for crimes to happen at that time.

16.What is the maximum of crimes in a Day on the STREET?

INsight : Base on the visualization we cansee that the most crimes on streets happened on August 4, 2024 base on the avxline. That day had the highest number of street crimes .

17.What is the Proportion of Arrests in SCHOOL - PUBLIC GROUNDS?

Insight : We can see in the result that theres a few people that is arrested on Schools while the not arrested is a lot i think these means they did not arrest those people so theres no student will get hurt if the suspect lost control or if he or she fight the police

18.How many crimes per Month in Restaurants?

INsight : base on the bar graph we can see that the crimes in restaurants happened every month, but some months had more than others.

19.How many crime on bar or taverns in chicago?

iNSIGHT : Base on the pie chart we can see that there is a lot of simple crime on bar or taverns and a few of pick pocket. i think pickpocket happens if the victim is drunk.

20.Yearly crimes on department store.

INsight : BAse on the barplot we can see that the theft in department stores went down each year. That means fewer stealing incidents happened over time.its like the store got better at stopping shoplifters.

21. How many theft crimes over time?

Insight : Base on the plot we can see that theft crimes went up around June and July 2024, then slowly went down by April 2025.

22.Which types of battery crimes happen most often, and are they usually domestic or more violent?

INsight : Base on the visualization most battery crimes are domestic and simple, they usually happen between people who know each other, like family or partners. More serious battery crimes, like those with weapons, and it happen less or not happening more times.

23.How many people get arrested for doing narcotic crimes??

Insight : Base on the pie chart we can clearly see that theres a few people only get arrested by doing narcotic crine and there are 95.8% taht is not arrested.

24.What kind of things get damaged the most in criminal cases?

Insight : Base on the barplot we can see that vehicle is the highest one that always get damage by criminal i thnk on car chases the car crasher and the second one is the properties i think that is connected to the vehicle when it hits the property they both get damaged.

25. Is there a correlation between the vehicle and property in criminal damage??

Insight : Base on the result we can see that there is the correlation between the two like i said on number 24 if the vehicle crashes on the property they both get damaged.

26.how many assualt crimes are there monthly in chicago??

Insight : Based on the plot we can see that assault crimes changed month to month like it rises and go down and rise again, but they dropped a lot by March 2022.

27.Number of robbery crimes in chicago daily?

Insight : Base on the plot even its so messy robbery crimes change a lot day by day, but some days had big rise it is like there is a robberry today but no tommorow like its alternate .

28.How many people got their vehicle got stolen in chicago?

Insight : BAse on the bar plot we can see that there is a lot of crimes on automobile that got stolen and a few for buses abd truck.

29.How many arrest rate on theft on chicago?

30.Weekday battery crimes on chicago.

INsight : Base on the bar plot we can see that there is a lot of battery crimes on sunday and in saturday it means criminals acts more on week end but on monday to friday those number are still high but the saturday is sunday is just higher battery crime rates.

31.correlation between Arrest and Domestic for criminal trespass.

Insight : Base on the heatmap domestic doesnt really affect the arrest on trespasses becaise there are not correlated.

32.Which areas in Chicago experience the most crime?

Insight : Based on the visualization heatmap we can see that the areas with the yellow color represent the highest crime rate and the orange for medium and the red for lowest crime rate and there a lot crimes that really happened on chicago.

33.What loation has the highest arrest rate in chicago?

Insight : Base on the bar graph we can see that the sidewalk has the highest arrest rate in chicago which means i think the criminal is being chased and got arrested on side walks and also some crimes inn chicago happens on it.

34.Arrest Rate by Description for a Specific Primary Type.

Insight : Base on the bar graph we can see that arrest type happens a lot of times if its related to firearm or the suspect have guns and less for reckless firearm discharges.

35. What is the correlation between Arrest and Domestic in Battery crimes in chicago?

Insight : Base on the result we can see that there is a correlation between Arrest and Domestic in Battery but that is too small so Domestic doesnt really affect the arrest in battery cases in chicago.